Evidence map›Paper›PMID 25074392›Full record

ReviewEuropean journal of clinical nutrition2014

The anti-obesity effects of green tea in human intervention and basic molecular studies.

J Huang, Y Wang, Z Xie, Y Zhou, Y Zhang, X Wan

Abstract readReview
PubMed Publisher
In one paragraph

Review in European journal of clinical nutrition, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
92citing papers in PubMed, 3 pooled it
8.0field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

92 citing papers in PubMed, 3 syntheses or guidelines pooled it, 235 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Trial
  6. Trial
  7. Trial
  8. Trial
  9. The safety of green tea extract supplementation in postmenopausal women at risk for breast cancer: results of the Minnesota Green Tea Trial.Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association · 2015
    Trial
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Green Tea: Current Knowledge and Issues.Foods (Basel, Switzerland) · 2025
    Review
  16. Frontiers in pharmacology · 2025
    Article
  17. Review
  18. Article
  19. Obesity-Associated Colorectal Cancer.International journal of molecular sciences · 2024
    Review
  20. Review

32 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

J HuangKey Laboratory of Tea Biochemistry and Biotechnology, Ministry of Agriculture and Ministry of Education, Anhui Agricultural University, Hefei, People's Republic of China.
Y WangKey Laboratory of Tea Biochemistry and Biotechnology, Ministry of Agriculture and Ministry of Education, Anhui Agricultural University, Hefei, People's Republic of China.
Z XieKey Laboratory of Tea Biochemistry and Biotechnology, Ministry of Agriculture and Ministry of Education, Anhui Agricultural University, Hefei, People's Republic of China.
Y ZhouKey Laboratory of Tea Biochemistry and Biotechnology, Ministry of Agriculture and Ministry of Education, Anhui Agricultural University, Hefei, People's Republic of China.
Y ZhangKey Laboratory of Tea Biochemistry and Biotechnology, Ministry of Agriculture and Ministry of Education, Anhui Agricultural University, Hefei, People's Republic of China.
X WanKey Laboratory of Tea Biochemistry and Biotechnology, Ministry of Agriculture and Ministry of Education, Anhui Agricultural University, Hefei, People's Republic of China.
Anhui Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many researchers have reported that obesity is a major risk factor for diabetes, cardiovascular diseases, several forms of cancer (such as breast, colon and prostate), pulmonary, osteoarticular and metabolic diseases in the past decades. Recently, the hypolipidemic and anti-obesity effects of green tea in animals and humans have slowly become a hot topic in nutritional and food science research. This review will up-date the information of the anti-obesity effects of green tea in human intervention and animal studies. During recent years, an increasing number of clinical trials have confirmed the beneficial effects of green tea on obesity. However, the optimal dose has not yet been established owing to the very different results from studies with a similar design, which may be caused by differences in the extent of obesity, dietary intake, physical activity intensity, the strength of subjects' compliance to test instruction, the genetic background of populations, body composition and dietary habits. Therefore, further investigations on a larger scale and with longer periods of observation and tighter controls are needed to define optimal doses in subjects with varying degrees of metabolic risk factors and to determine differences in beneficial effects among diverse populations. Moreover, data from laboratory studies have shown that green tea has important roles in fat metabolism by reducing food intake, interrupting lipid emulsification and absorption, suppressing adipogenesis and lipid synthesis and increasing energy expenditure via thermogenesis, fat oxidation and fecal lipid excretion. However, the exact molecular mechanisms remain elusive.

Indexed as

Camellia sinensisPhytotherapyTeaAdolescentAdultAgedAnti-Obesity AgentsAntioxidantsCatechinChildHumansLipid MetabolismMiddle AgedObesityPolyphenolsWeight LossAnti-Obesity AgentsAntioxidantsCatechinepigallocatechin gallatePolyphenolsTea

Identifiers

PMID25074392
OpenAlexW2028250360

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.